{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7fc13177-7d9d-4959-bbe9-fa26d60ea786",
   "metadata": {},
   "source": [
    "# Make Reader\n",
    "\n",
    "We show how LlamaIndex can fit with your Make.com workflow by sending the GPT Index response to a scenario webhook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2289d27",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import sys\n",
    "\n",
    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
    "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f90c60a6-50b3-4b66-abf3-9723dac8a045",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
    "from llama_index.readers import MakeWrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd8885c5-39e2-444b-9666-5032ab4cb50d",
   "metadata": {},
   "outputs": [],
   "source": [
    "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()\n",
    "index = VectorStoreIndex.from_documents(documents=documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5f7d888-01ed-40f7-9216-6c7340b229bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set Logging to DEBUG for more detailed outputs\n",
    "# query index\n",
    "query_str = \"What did the author do growing up?\"\n",
    "query_engine = index.as_query_engine()\n",
    "response = query_engine.query(query_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eaf06ad9-ba04-42fb-a7c8-daf7a5320b53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Send response to Make.com webhook\n",
    "wrapper = MakeWrapper()\n",
    "wrapper.pass_response_to_webhook(\n",
    "    \"<webhook_url>,\n",
    "    response,\n",
    "    query_str\n",
    ")"
   ]
  }
 ],
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